Import and warehouse data

!pip install lucifer-ml

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import pandas as pd 
import numpy as np 
import seaborn as sns
import matplotlib.pyplot as plt 
from collections import Counter
from sklearn.model_selection import train_test_split
import plotly.graph_objects as go
import plotly.express as px
from luciferml.supervised import classification as cls

Telecom = pd.read_csv('TelcomCustomer-Churn_12.csv')
print(Telecom.shape)
Telecom.head()

(7043, 21)

	customerID	gender	SeniorCitizen	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	...	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	7590-VHVEG	Female	0	Yes	No	1	No	No phone service	DSL	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	29.85	29.85	No
1	5575-GNVDE	Male	0	No	No	34	Yes	No	DSL	Yes	...	Yes	No	No	No	One year	No	Mailed check	56.95	1889.5	No
2	3668-QPYBK	Male	0	No	No	2	Yes	No	DSL	Yes	...	No	No	No	No	Month-to-month	Yes	Mailed check	53.85	108.15	Yes
3	7795-CFOCW	Male	0	No	No	45	No	No phone service	DSL	Yes	...	Yes	Yes	No	No	One year	No	Bank transfer (automatic)	42.30	1840.75	No
4	9237-HQITU	Female	0	No	No	2	Yes	No	Fiber optic	No	...	No	No	No	No	Month-to-month	Yes	Electronic check	70.70	151.65	Yes

5 rows × 21 columns

Telecom.dtypes

customerID           object
gender               object
SeniorCitizen         int64
Partner              object
Dependents           object
tenure                int64
PhoneService         object
MultipleLines        object
InternetService      object
OnlineSecurity       object
OnlineBackup         object
DeviceProtection     object
TechSupport          object
StreamingTV          object
StreamingMovies      object
Contract             object
PaperlessBilling     object
PaymentMethod        object
MonthlyCharges      float64
TotalCharges         object
Churn                object
dtype: object

print(Telecom.isnull().sum())

customerID          0
gender              0
SeniorCitizen       0
Partner             0
Dependents          0
tenure              0
PhoneService        0
MultipleLines       0
InternetService     0
OnlineSecurity      0
OnlineBackup        0
DeviceProtection    0
TechSupport         0
StreamingTV         0
StreamingMovies     0
Contract            0
PaperlessBilling    0
PaymentMethod       0
MonthlyCharges      0
TotalCharges        0
Churn               0
dtype: int64

Data cleansing, Data analysis and visualisation

Telecom.describe().T.style.bar(
    subset=['mean'],
    color='Reds').background_gradient(
    subset=['std'], cmap='ocean').background_gradient(subset=['50%'], cmap='PuBu')

	count	mean	std	min	25%	50%	75%	max
SeniorCitizen	7043.000000	0.162147	0.368612	0.000000	0.000000	0.000000	0.000000	1.000000
tenure	7043.000000	32.371149	24.559481	0.000000	9.000000	29.000000	55.000000	72.000000
MonthlyCharges	7043.000000	64.761692	30.090047	18.250000	35.500000	70.350000	89.850000	118.750000

Telecom.describe()

	SeniorCitizen	tenure	MonthlyCharges
count	7043.000000	7043.000000	7043.000000
mean	0.162147	32.371149	64.761692
std	0.368612	24.559481	30.090047
min	0.000000	0.000000	18.250000
25%	0.000000	9.000000	35.500000
50%	0.000000	29.000000	70.350000
75%	0.000000	55.000000	89.850000
max	1.000000	72.000000	118.750000

Telecom.shape

(7043, 21)

churn_count = Telecom['Churn'].value_counts()
print(churn_count)
churn_count.plot.pie()

No     5174
Yes    1869
Name: Churn, dtype: int64

<AxesSubplot:ylabel='Churn'>

plt.figure(figsize=(6,5))
sns.countplot(x="Churn", data=Telecom, palette='viridis');

Telecom.columns

Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],
      dtype='object')

def boxhistplot(columns,data):
    fig = px.histogram(Telecom, x = Telecom[column], color = 'Churn')
    fig.show()
    fig2 = px.box(Telecom, x = Telecom[column], color = 'Churn')
    fig2.show()

col = ['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',
       'PaymentMethod', 'MonthlyCharges', 'TotalCharges']
for column in col:
    boxhistplot(column,Telecom)

sns.heatmap(Telecom.corr(), annot=True, cmap="flag")

<AxesSubplot:>

sns.pairplot(Telecom, hue="Churn", palette="magma")

<seaborn.axisgrid.PairGrid at 0x202294d30a0>

Data pre-processing, Model training, testing and tuning

 from luciferml.supervised.classification import Classification
 dataset = pd.read_csv('TelcomCustomer-Churn_12.csv')

 features = [ 'gender', 'SeniorCitizen', 'Partner', 'Dependents',
       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',
       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',
       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'MonthlyCharges', 'TotalCharges']
X = Telecom[features]
y = Telecom['Churn']
 classifier = Classification(predictor = 'lr')
 classifier.fit(X, y)
 result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training Logistic Regression on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[935 101]
 [159 214]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.815471965933286
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 79.84 %
Standard Deviation: 1.15 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  0.556931734085083 seconds 

classifier = Classification(predictor = 'svm')
classifier.fit(X, y)
result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training Support Vector Machine on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[946  90]
 [177 196]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.8105039034776437
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 79.43 %
Standard Deviation: 0.53 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  10.523306369781494 seconds 

classifier = Classification(predictor = 'knn')
classifier.fit(X, y)
result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training K-Nearest Neighbours on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[889 147]
 [187 186]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.7629524485450674
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 76.06 %
Standard Deviation: 1.01 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  1.1291532516479492 seconds 

classifier = Classification(predictor = 'dt')
classifier.fit(X, y)
result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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╚══════╝░╚═════╝░░╚════╝░╚═╝╚═╝░░░░░╚══════╝╚═╝░░╚═╝░░░░░░╚═╝░░░░░╚═╝╚══════╝

Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training Decision Tree Classifier on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[846 190]
 [203 170]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.7210787792760823
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 73.20 %
Standard Deviation: 2.12 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  0.5701887607574463 seconds 

classifier = Classification(predictor = 'nb')
classifier.fit(X, y)
result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training Naive Bayes Classifier on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[660 376]
 [ 47 326]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.6997870830376153
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 68.73 %
Standard Deviation: 1.98 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  0.2935006618499756 seconds 

classifier = Classification(predictor = 'rfc')
classifier.fit(X, y)
result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training Random Forest Classifier on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[935 101]
 [194 179]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.7906316536550745
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 78.01 %
Standard Deviation: 1.28 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  8.826218843460083 seconds 

classifier = Classification(predictor = 'gbc')
classifier.fit(X, y)
result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training Gradient Boosting Classifier on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[941  95]
 [180 193]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.8048261178140526
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 79.82 %
Standard Deviation: 1.61 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  16.009963989257812 seconds 

classifier = Classification(predictor = 'bag')
classifier.fit(X, y)
result = classifier.result()
accuracy_scores[result['Classifier']] = result['Accuracy']

          
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Started LuciferML 

Checking if labels or features are categorical! [*]

Features are Categorical

Encoding Features [*]

Encoding Features Done [ ✓ ]

Labels are not categorical [ ✓ ]

Checking for Categorical Variables Done [ ✓ ]

Checking for Sparse Matrix [*]

Splitting Data into Train and Validation Sets [*]

Splitting Done [ ✓ ]

Scaling Training and Test Sets [*]

Scaling Done [ ✓ ]

Training Bagging Classifier on Training Set [*]

Model Training Done [ ✓ ]

Predicting Data [*]

Data Prediction Done [ ✓ ]

Making Confusion Matrix [*]
[[935 101]
 [210 163]]

Confusion Matrix Done [ ✓ ]

Evaluating Model Performance [*]
Validation Accuracy is : 0.7792760823278921
Evaluating Model Performance [ ✓ ]

Applying K-Fold Cross Validation [*]
Accuracy: 77.09 %
Standard Deviation: 1.37 %
K-Fold Cross Validation [ ✓ ]

Complete [ ✓ ]

Time Elapsed :  2.5434954166412354 seconds 

plt.figure(figsize=(15, 6))
model_accuracies = list(accuracy_scores.values())
model_names = list(accuracy_scores.keys())
sns.barplot(x=model_accuracies, y=model_names, palette='mako')

<AxesSubplot:>

The model with highest K-Fold Validation Accuracy score is Logistic Regression with an accuracy of 79.84.

Conclusion

The company can now look at the data and predict the customers likes and dislikes. The bivariate and multivariate analysis can give more insights for the company to take decisons wisely and strategize their markeing value chain. This will make executives to conduct the effective customer retention programmes.

To get more insight on churn, pertaining to this data set, the parameters like internet speed and international calling should be included. I enjoyed working on this data set and it was effective.